An accurate real-time method to detect the smile facial expression
Resumo
The rapid advancement of technology has significantly improved our ability to interact with electronic devices and derive meaning from this interaction. One crucial aspect of human-environment interaction is emotion recognition, which allows us to understand and appropriately respond to emotional cues. Among these cues, a smiling facial expression represents contentment and a positive response to situations, usually indicating happiness. Facial expression recognition techniques have been extensively utilized by researchers and scientists in both academic and commercial settings because the analysis of users’ facial expressions can provide valuable insights into computer system behavior and usability, making it an important area of research. In this paper, we propose a method for detecting smile facial expressions by focusing on the mouth region, which plays a vital role in smile characterization and identification. Our approach innovates utilizing normalized facial reference points, commonly known as landmarks, as input for machine learning classifiers. These normalized landmarks are two-dimensional coordinates, enabling low-cost and real-time processing, particularly suitable for devices with limited processing capabilities, such as cellphones. Remarkably, our method exhibits a real-time performance accuracy exceeding 95%. Our research contributes to producing more consistent and accurate outcomes in smile detection, surpassing existing results from the literature.
Palavras-chave:
Multimedia Processing, Affective Computing, AI, Machine Learning, Deep Learning, Multimodal Interaction, Computer Vision, Smile Detection, Facial Patterns
Referências
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Irshaad Ali and Mohit Dua. 2019. Smile Detection: Current Trends, Challenges and Future Perspective. In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, Coimbatore, India, 151–156. https://doi.org/10.1109/ICECA.2019.8822000
Mauricio Alvarez, David Luengo, and Neil Lawrence. 2013. Linear Latent Force Models Using Gaussian Processes. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 11 (Nov 2013), 2693–2705. https://doi.org/10.1109/TPAMI.2013.86
Mitra B., Sharma K., Acharya S., Mishra P., and Guglani A.2022. Real-time Smile Detection using Integrated ML Model. In 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, Madurai, India, 1374–1381. https://doi.org/10.1109/ICICCS53718.2022.9788399
Hugo Bohy, Kevin El Haddad, and Thierry Dutoit. 2022. A New Perspective on Smiling and Laughter Detection: Intensity Levels Matter. In 2022 10th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, Nara, Japan, 1–8. https://doi.org/10.1109/ACII55700.2022.9953896
Guilherme Campos, Arthur Zimek, Joerg Sander, Ricardo Campello, Barbora Micenková, Erich Schubert, Ira Assent, and Michael Houle. 2016. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery 30 (07 2016). https://doi.org/10.1007/s10618-015-0444-8
V. Chaugule, D. Abhishek, A. Vijayakumar, P. B. Ramteke, and S. G. Koolagudi. 2016. Product review based on optimized facial expression detection. In 2016 Ninth International Conference on Contemporary Computing (IC3). IEEE, Noida, India, 1–6. https://doi.org/10.1109/IC3.2016.7880213
Yufang Cheng and Shuhui Ling. 2008. 3D Animated Facial Expression and Autism in Taiwan. In IEEE International Conference on Advanced Learning Technologies (ICALT 2008). IEEE Computer Society, Los Alamitos, CA, USA, 17–19. https://doi.org/10.1109/ICALT.2008.220
Francois Chollet. 2017. Deep Learning with Python (1st ed.). Manning Publications Co., Greenwich, CT, USA.
Dongshun Cui, Guang-Bin Huang, and Tianchi Liu. 2018. ELM based smile detection using Distance Vector. Pattern Recognition 79 (2018), 356–369. https://doi.org/10.1016/j.patcog.2018.02.019
D Dalal and B. Triggs. 2005. Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), Vol. 1. IEEE, San Diego, CA, USA, 886–893 vol. 1. https://doi.org/10.1109/CVPR.2005.177
C. Darwin. 1916. The Expression of the Emotions in Man and Animals. D. Appleton, Universidade da Califórnia, USA. https://books.google.com.br/books?id=N5MMAQAAIAAJ
Alex Davies and Zoubin Ghahramani. 2014. The Random Forest Kernel and other kernels for big data from random partitions. arxiv:1402.4293 [stat.ML]
Paul Ekman and Wallace V. Friesen. 1971. Constants across cultures in the face and emotion. Journal of Personality and Social Psychology 17, 2 (1971), 124–129. https://doi.org/10.1037/h0030377
R. J. Ekman P., Davidson. 1993. Voluntary Smiling Changes Regional Brain Activity. Psychological Science 4, 5 (1993), 342–345. https://doi.org/10.1111/j.1467-9280.1993.tb00576.x
Marmolejo-Ramos F., Murata A., and et al.2020. Your face and moves seem happier when I smile. Facial action influences the perception of emotional faces and biological motion stimuli. Experimental Psychology 67, 1 (2020), 14–22
Yuan Gao, Hong Liu, Pingping Wu, and Can Wang. 2016. A new descriptor of gradients Self-Similarity for smile detection in unconstrained scenarios. Neurocomputing 174 (2016), 1077 – 1086. https://doi.org/10.1016/j.neucom.2015.10.022
Gabriel Garrido and Prateek Joshi. 2018. OpenCV 3.X with Python By Example: Make the most of OpenCV and Python to build applications for object recognition and augmented reality (2nd ed.). Packt Publishing, US
A. T. Ghorbani, G; Targhi and M. Dehshibi. 2015. HOG and LBP: Towards a robust face recognition system. In 2015 Tenth International Conference on Digital Information Management (ICDIM). IEEE, Jeju, South Korea, 1201–1209
Rafael C Gonzales and Richard E. Woods. 2008. Digital Image Processing (3rd ed.). Pearson, New Jersey, US
Trevor Hastie, Robert Tibshirani, and Jerome Friedman. 2009. The elements of statistical learning: data mining, inference and prediction (2 ed.). Springer, USA. http://www-stat.stanford.edu/tibs/ElemStatLearn/
Yu-Hao Huang. 2009. FACE DETECTION AND SMILE DETECTION. IPPR Conference on Computer Vision, Graphics and Image Processing 23 (2009), 211–221
Chen J., Ou Q., Chi Z., and Fu H.2017. Smile detection in the wild with deep convolutional neural networks. Machine Vision and Applications 28 (2017), 173–183. https://doi.org/10.1007/s00138-016-0817-z
L Jia, J; Zhang and Lianhong Cai. 2010. Facial expression synthesis based on motion patterns learned from face database. In 2010 IEEE International Conference on Image Processing. IEEE Computer Society, Hong Kong, 3973–3976. https://doi.org/10.1109/ICIP.2010.5653914
Tara L. Kraft and Sarah D. Pressman. 2012. Grin and Bear It: The Influence of Manipulated Facial Expression on the Stress Response. Psychological Science 23, 11 (2012), 1372–1378. https://doi.org/10.1177/0956797612445312
A. Kumar, K.M. Baalamurugan, and B. Balamurugan. 2022. Real-Time Facial Components Detection Using Haar Classifiers. In 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, Salem, India, 01–08. https://doi.org/10.1109/ICAAIC53929.2022.9793034
Uttama Lahiri, Esube Bekele, Elizabeth Dohrmann, Zachary Warren, and Nilanjan Sarkar. 2011. Design of a Virtual Reality Based Adaptive Response Technology for Children with Autism Spectrum Disorder. In Affective Computing and Intelligent Interaction, Sidney D’Mello, Arthur Graesser, Björn Schuller, and Jean-Claude Martin (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 165–174
Shan Li and Weihong Deng. 2018. Deep Facial Expression Recognition: A Survey. Computing Research Repository (CoRR) abs/1804.08348 (2018), 1322–1330. https://doi.org/10.1109/TAFFC.2020.2981446
Katsutoshi Masai, Monica Perusquía-Hernández, Maki Sugimoto, Shiro Kumano, and Toshitaka Kimura. 2022. Consistent Smile Intensity Estimation from Wearable Optical Sensors. In 2022 10th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, Nara, Japan, 1–8. https://doi.org/10.1109/ACII55700.2022.9953867
Rezwan Matin and Damian Valles. 2020. A Speech Emotion Recognition Solution-based on Support Vector Machine for Children with Autism Spectrum Disorder to Help Identify Human Emotions. In 2020 Intermountain Engineering, Technology and Computing (IETC). IEEE, Orem-US, 1–6. https://doi.org/10.1109/IETC47856.2020.9249147
Dhwani Mehta, Mohammad Faridul Haque Siddiqui, and Ahmad Y. Javaid. 2018. Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality. Sensors 18, 2 (2018), 1232–1243. https://doi.org/10.3390/s18020416
A Monzo, D; Albiol and M. J. Mossi. 2010. A Comparative Study of Facial Landmark Localization Methods for Face Recognition Using HOG descriptors. In 2010 20th International Conference on Pattern Recognition. IEEE, Istanbul, Turkey, 1330–1333. https://doi.org/10.1109/ICPR.2010.1145
Coles NA, March DS, Marmolejo-Ramos F, and et al.2022. A multi-lab test of the facial feedback hypothesis by the Many Smiles Collaboration. Nat Hum Behav 6, 12 (Dec 2022), 1731–1742. https://doi.org/10.1038/s41562-022-01458-9
Hend Maher Obaya, Amany Mahmoud Sarhan, and Marwa Mahmoud Badr. 2023. Deeply Smile Detection Based on Discriminative Features with Modified LeNet-5 Network. Journal of Engineering Research (ERJ) 7, 2 (2023), 22–32. https://doi.org/10.21608/ERJENG.2023.194599.1156
Carlos E. Oliveira Junior, Leo L.; Thomaz. 2006. Captura e Alinhamento de Imagens: Um Banco de Faces Brasileiro. Technical Report. Centro Universitário da Fundação Educacional Inaciana, FEI, São Bernardo do Campo,SP,Brasil. http://www.fei.edu.br/cet/publications.html
Ozlem Ozbudak, Mürvet Kircı, Yüksel Çakir, and Ece Olcay Guneş. 2010. Effects of the facial and racial features on gender classification. In Melecon 2010 - 2010 15th IEEE Mediterranean Electrotechnical Conference. IEEE, Valetta-Malta, 26–29. https://doi.org/10.1109/MELCON.2010.5476346
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, 2011. Scikit-learn: Machine learning in Python. Journal of machine learning research 12, Oct (2011), 2825–2830.
P.Jonathon Phillips, Harry Wechsler, Jeffery Huang, and Patrick J. Rauss. 1998. The FERET database and evaluation procedure for face-recognition algorithms. Image and Vision Computing 16, 5 (1998), 295 – 306. https://doi.org/10.1016/S0262-8856(97)00070-X
Leonard R. Rubin. 1974. The Anatomy of a smile: its importance in the treatment of facial paralysis. Plastic and Reconstructive Surgery 53, 4 (1974), 1130–1142
Jia S, Wang S, Hu C., Webster PJ, and Li X.2021. Detection of Genuine and Posed Facial Expressions of Emotion: Databases and Methods. Front. Psychol. - Sec. Perception Science 11 (15 January 2021), 12p. https://doi.org/10.3389/fpsyg.2020.580287
Park S, Lee K, Lim JA, Ko H, Kim T, Lee JI, Kim H, Han SJ, Kim JS, Park S, Lee JY, and Lee EC. 2020. Differences in Facial Expressions between Spontaneous and Posed Smiles: Automated Method by Action Units and Three-Dimensional Facial Landmarks. Sensors (Basel) 20, 4 (21 Feb 2020), 1199. https://doi.org/10.3390/s20041199
C. Shan. 2012. Smile detection by boosting pixel differences. IEEE Transactions on Image Processing 21, 1 (2012), 431–436. cited By 65. https://doi.org/10.1109/TIP.2011.2161587
Cen Shengcai, Luo Haokun, Huang Jinghan, Shi Wurui, and Chen Xueyun. 2022. Pre-Trained Feature Fusion and Multidomain Identification Generative Adversarial Network for Face Frontalization. IEEE Access 10 (2022), 77872–77882. https://doi.org/10.1109/ACCESS.2022.3193386
Paul F. Smith, Siva Ganesh, and Ping Liu. 2013. A comparison of random forest regression and multiple linear regression for prediction in neuroscience. Journal of Neuroscience Methods 220, 1 (2013), 85–91. https://doi.org/10.1016/j.jneumeth.2013.08.024
Rafael Luiz Testa, Cléber Gimenez Corrêa, Ariane Machado-Lima, and Fátima L. S. Nunes. 2019. Synthesis of Facial Expressions in Photographs: Characteristics, Approaches, and Challenges. ACM Comput. Surv. 51, 6, Article 124 (jan 2019), 35 pages. https://doi.org/10.1145/3292652
Nguyen Minh Tu, Nguyen Quoc Khanh, Kazunori Kotani, and Siritanawan Prarinya. 2019. Towards recognizing facial expressions at deeper level: Discriminating genuine and fake smiles from a sequence of images. In 2019 6th NAFOSTED Conference on Information and Computer Science (NICS). IEEE, Hanoi, Vietnam, 387–392. https://doi.org/10.1109/NICS48868.2019.9023790
Jean Vaillancourt. 2010. Statistical Methods for Data Mining and Knowledge Discovery. In Proceedings of the 8th International Conference on Formal Concept Analysis (Agadir, Morocco) (ICFCA’10). Springer-Verlag, Berlin, Heidelberg, 51–60.
Paul Viola and Michael J. Jones. 2001. Robust Real-Time Face Detection. International Journal of Computer Vision 57, 2 (2001), 137–154. https://doi.org/10.1023/B:VISI.0000013087.49260.fb
J. Whitehill, G. Littlewort, I. Fasel, M. Bartlett, and J. Movellan. 2009. Toward Practical Smile Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 11 (Nov 2009), 2106–2111. https://doi.org/10.1109/TPAMI.2009.42
Yue Wu and Qiang Ji. 2018. Facial Landmark Detection: A Literature Survey. International Journal of Computer Vision 2 (2018), 115–142. https://doi.org/10.1007/s11263-018-1097-z
W. Xie, L. Sheb, M. Yang, and Q. Hou. 2015. Lighting difference based wrinkle mapping for expression synthesis. In 8th International Congress on Image and Signal Processing (CISP). IEEE, Shenyang, China, 636–641. https://doi.org/10.1109/CISP.2015.7407956
Ruiqi Zhao, Yan Wang, and Aleix M. Martinez. 2018. A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image. IEEE Transactions on Pattern Analysis and Machine Intelligence 40, 12 (2018), 3059–3066. https://doi.org/10.1109/TPAMI.2017.2772922
Xiaoming Zhao and Shiqing Zhang. 2016. A Review on Facial Expression Recognition: Feature Extraction and Classification. IETE Technical Review 33, 5 (2016), 505–517. https://doi.org/10.1080/02564602.2015.1117403
Irshaad Ali and Mohit Dua. 2019. Smile Detection: Current Trends, Challenges and Future Perspective. In 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, Coimbatore, India, 151–156. https://doi.org/10.1109/ICECA.2019.8822000
Mauricio Alvarez, David Luengo, and Neil Lawrence. 2013. Linear Latent Force Models Using Gaussian Processes. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 11 (Nov 2013), 2693–2705. https://doi.org/10.1109/TPAMI.2013.86
Mitra B., Sharma K., Acharya S., Mishra P., and Guglani A.2022. Real-time Smile Detection using Integrated ML Model. In 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, Madurai, India, 1374–1381. https://doi.org/10.1109/ICICCS53718.2022.9788399
Hugo Bohy, Kevin El Haddad, and Thierry Dutoit. 2022. A New Perspective on Smiling and Laughter Detection: Intensity Levels Matter. In 2022 10th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, Nara, Japan, 1–8. https://doi.org/10.1109/ACII55700.2022.9953896
Guilherme Campos, Arthur Zimek, Joerg Sander, Ricardo Campello, Barbora Micenková, Erich Schubert, Ira Assent, and Michael Houle. 2016. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery 30 (07 2016). https://doi.org/10.1007/s10618-015-0444-8
V. Chaugule, D. Abhishek, A. Vijayakumar, P. B. Ramteke, and S. G. Koolagudi. 2016. Product review based on optimized facial expression detection. In 2016 Ninth International Conference on Contemporary Computing (IC3). IEEE, Noida, India, 1–6. https://doi.org/10.1109/IC3.2016.7880213
Yufang Cheng and Shuhui Ling. 2008. 3D Animated Facial Expression and Autism in Taiwan. In IEEE International Conference on Advanced Learning Technologies (ICALT 2008). IEEE Computer Society, Los Alamitos, CA, USA, 17–19. https://doi.org/10.1109/ICALT.2008.220
Francois Chollet. 2017. Deep Learning with Python (1st ed.). Manning Publications Co., Greenwich, CT, USA.
Dongshun Cui, Guang-Bin Huang, and Tianchi Liu. 2018. ELM based smile detection using Distance Vector. Pattern Recognition 79 (2018), 356–369. https://doi.org/10.1016/j.patcog.2018.02.019
D Dalal and B. Triggs. 2005. Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), Vol. 1. IEEE, San Diego, CA, USA, 886–893 vol. 1. https://doi.org/10.1109/CVPR.2005.177
C. Darwin. 1916. The Expression of the Emotions in Man and Animals. D. Appleton, Universidade da Califórnia, USA. https://books.google.com.br/books?id=N5MMAQAAIAAJ
Alex Davies and Zoubin Ghahramani. 2014. The Random Forest Kernel and other kernels for big data from random partitions. arxiv:1402.4293 [stat.ML]
Paul Ekman and Wallace V. Friesen. 1971. Constants across cultures in the face and emotion. Journal of Personality and Social Psychology 17, 2 (1971), 124–129. https://doi.org/10.1037/h0030377
R. J. Ekman P., Davidson. 1993. Voluntary Smiling Changes Regional Brain Activity. Psychological Science 4, 5 (1993), 342–345. https://doi.org/10.1111/j.1467-9280.1993.tb00576.x
Marmolejo-Ramos F., Murata A., and et al.2020. Your face and moves seem happier when I smile. Facial action influences the perception of emotional faces and biological motion stimuli. Experimental Psychology 67, 1 (2020), 14–22
Yuan Gao, Hong Liu, Pingping Wu, and Can Wang. 2016. A new descriptor of gradients Self-Similarity for smile detection in unconstrained scenarios. Neurocomputing 174 (2016), 1077 – 1086. https://doi.org/10.1016/j.neucom.2015.10.022
Gabriel Garrido and Prateek Joshi. 2018. OpenCV 3.X with Python By Example: Make the most of OpenCV and Python to build applications for object recognition and augmented reality (2nd ed.). Packt Publishing, US
A. T. Ghorbani, G; Targhi and M. Dehshibi. 2015. HOG and LBP: Towards a robust face recognition system. In 2015 Tenth International Conference on Digital Information Management (ICDIM). IEEE, Jeju, South Korea, 1201–1209
Rafael C Gonzales and Richard E. Woods. 2008. Digital Image Processing (3rd ed.). Pearson, New Jersey, US
Trevor Hastie, Robert Tibshirani, and Jerome Friedman. 2009. The elements of statistical learning: data mining, inference and prediction (2 ed.). Springer, USA. http://www-stat.stanford.edu/tibs/ElemStatLearn/
Yu-Hao Huang. 2009. FACE DETECTION AND SMILE DETECTION. IPPR Conference on Computer Vision, Graphics and Image Processing 23 (2009), 211–221
Chen J., Ou Q., Chi Z., and Fu H.2017. Smile detection in the wild with deep convolutional neural networks. Machine Vision and Applications 28 (2017), 173–183. https://doi.org/10.1007/s00138-016-0817-z
L Jia, J; Zhang and Lianhong Cai. 2010. Facial expression synthesis based on motion patterns learned from face database. In 2010 IEEE International Conference on Image Processing. IEEE Computer Society, Hong Kong, 3973–3976. https://doi.org/10.1109/ICIP.2010.5653914
Tara L. Kraft and Sarah D. Pressman. 2012. Grin and Bear It: The Influence of Manipulated Facial Expression on the Stress Response. Psychological Science 23, 11 (2012), 1372–1378. https://doi.org/10.1177/0956797612445312
A. Kumar, K.M. Baalamurugan, and B. Balamurugan. 2022. Real-Time Facial Components Detection Using Haar Classifiers. In 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC). IEEE, Salem, India, 01–08. https://doi.org/10.1109/ICAAIC53929.2022.9793034
Uttama Lahiri, Esube Bekele, Elizabeth Dohrmann, Zachary Warren, and Nilanjan Sarkar. 2011. Design of a Virtual Reality Based Adaptive Response Technology for Children with Autism Spectrum Disorder. In Affective Computing and Intelligent Interaction, Sidney D’Mello, Arthur Graesser, Björn Schuller, and Jean-Claude Martin (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 165–174
Shan Li and Weihong Deng. 2018. Deep Facial Expression Recognition: A Survey. Computing Research Repository (CoRR) abs/1804.08348 (2018), 1322–1330. https://doi.org/10.1109/TAFFC.2020.2981446
Katsutoshi Masai, Monica Perusquía-Hernández, Maki Sugimoto, Shiro Kumano, and Toshitaka Kimura. 2022. Consistent Smile Intensity Estimation from Wearable Optical Sensors. In 2022 10th International Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, Nara, Japan, 1–8. https://doi.org/10.1109/ACII55700.2022.9953867
Rezwan Matin and Damian Valles. 2020. A Speech Emotion Recognition Solution-based on Support Vector Machine for Children with Autism Spectrum Disorder to Help Identify Human Emotions. In 2020 Intermountain Engineering, Technology and Computing (IETC). IEEE, Orem-US, 1–6. https://doi.org/10.1109/IETC47856.2020.9249147
Dhwani Mehta, Mohammad Faridul Haque Siddiqui, and Ahmad Y. Javaid. 2018. Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality. Sensors 18, 2 (2018), 1232–1243. https://doi.org/10.3390/s18020416
A Monzo, D; Albiol and M. J. Mossi. 2010. A Comparative Study of Facial Landmark Localization Methods for Face Recognition Using HOG descriptors. In 2010 20th International Conference on Pattern Recognition. IEEE, Istanbul, Turkey, 1330–1333. https://doi.org/10.1109/ICPR.2010.1145
Coles NA, March DS, Marmolejo-Ramos F, and et al.2022. A multi-lab test of the facial feedback hypothesis by the Many Smiles Collaboration. Nat Hum Behav 6, 12 (Dec 2022), 1731–1742. https://doi.org/10.1038/s41562-022-01458-9
Hend Maher Obaya, Amany Mahmoud Sarhan, and Marwa Mahmoud Badr. 2023. Deeply Smile Detection Based on Discriminative Features with Modified LeNet-5 Network. Journal of Engineering Research (ERJ) 7, 2 (2023), 22–32. https://doi.org/10.21608/ERJENG.2023.194599.1156
Carlos E. Oliveira Junior, Leo L.; Thomaz. 2006. Captura e Alinhamento de Imagens: Um Banco de Faces Brasileiro. Technical Report. Centro Universitário da Fundação Educacional Inaciana, FEI, São Bernardo do Campo,SP,Brasil. http://www.fei.edu.br/cet/publications.html
Ozlem Ozbudak, Mürvet Kircı, Yüksel Çakir, and Ece Olcay Guneş. 2010. Effects of the facial and racial features on gender classification. In Melecon 2010 - 2010 15th IEEE Mediterranean Electrotechnical Conference. IEEE, Valetta-Malta, 26–29. https://doi.org/10.1109/MELCON.2010.5476346
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, 2011. Scikit-learn: Machine learning in Python. Journal of machine learning research 12, Oct (2011), 2825–2830.
P.Jonathon Phillips, Harry Wechsler, Jeffery Huang, and Patrick J. Rauss. 1998. The FERET database and evaluation procedure for face-recognition algorithms. Image and Vision Computing 16, 5 (1998), 295 – 306. https://doi.org/10.1016/S0262-8856(97)00070-X
Leonard R. Rubin. 1974. The Anatomy of a smile: its importance in the treatment of facial paralysis. Plastic and Reconstructive Surgery 53, 4 (1974), 1130–1142
Jia S, Wang S, Hu C., Webster PJ, and Li X.2021. Detection of Genuine and Posed Facial Expressions of Emotion: Databases and Methods. Front. Psychol. - Sec. Perception Science 11 (15 January 2021), 12p. https://doi.org/10.3389/fpsyg.2020.580287
Park S, Lee K, Lim JA, Ko H, Kim T, Lee JI, Kim H, Han SJ, Kim JS, Park S, Lee JY, and Lee EC. 2020. Differences in Facial Expressions between Spontaneous and Posed Smiles: Automated Method by Action Units and Three-Dimensional Facial Landmarks. Sensors (Basel) 20, 4 (21 Feb 2020), 1199. https://doi.org/10.3390/s20041199
C. Shan. 2012. Smile detection by boosting pixel differences. IEEE Transactions on Image Processing 21, 1 (2012), 431–436. cited By 65. https://doi.org/10.1109/TIP.2011.2161587
Cen Shengcai, Luo Haokun, Huang Jinghan, Shi Wurui, and Chen Xueyun. 2022. Pre-Trained Feature Fusion and Multidomain Identification Generative Adversarial Network for Face Frontalization. IEEE Access 10 (2022), 77872–77882. https://doi.org/10.1109/ACCESS.2022.3193386
Paul F. Smith, Siva Ganesh, and Ping Liu. 2013. A comparison of random forest regression and multiple linear regression for prediction in neuroscience. Journal of Neuroscience Methods 220, 1 (2013), 85–91. https://doi.org/10.1016/j.jneumeth.2013.08.024
Rafael Luiz Testa, Cléber Gimenez Corrêa, Ariane Machado-Lima, and Fátima L. S. Nunes. 2019. Synthesis of Facial Expressions in Photographs: Characteristics, Approaches, and Challenges. ACM Comput. Surv. 51, 6, Article 124 (jan 2019), 35 pages. https://doi.org/10.1145/3292652
Nguyen Minh Tu, Nguyen Quoc Khanh, Kazunori Kotani, and Siritanawan Prarinya. 2019. Towards recognizing facial expressions at deeper level: Discriminating genuine and fake smiles from a sequence of images. In 2019 6th NAFOSTED Conference on Information and Computer Science (NICS). IEEE, Hanoi, Vietnam, 387–392. https://doi.org/10.1109/NICS48868.2019.9023790
Jean Vaillancourt. 2010. Statistical Methods for Data Mining and Knowledge Discovery. In Proceedings of the 8th International Conference on Formal Concept Analysis (Agadir, Morocco) (ICFCA’10). Springer-Verlag, Berlin, Heidelberg, 51–60.
Paul Viola and Michael J. Jones. 2001. Robust Real-Time Face Detection. International Journal of Computer Vision 57, 2 (2001), 137–154. https://doi.org/10.1023/B:VISI.0000013087.49260.fb
J. Whitehill, G. Littlewort, I. Fasel, M. Bartlett, and J. Movellan. 2009. Toward Practical Smile Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 31, 11 (Nov 2009), 2106–2111. https://doi.org/10.1109/TPAMI.2009.42
Yue Wu and Qiang Ji. 2018. Facial Landmark Detection: A Literature Survey. International Journal of Computer Vision 2 (2018), 115–142. https://doi.org/10.1007/s11263-018-1097-z
W. Xie, L. Sheb, M. Yang, and Q. Hou. 2015. Lighting difference based wrinkle mapping for expression synthesis. In 8th International Congress on Image and Signal Processing (CISP). IEEE, Shenyang, China, 636–641. https://doi.org/10.1109/CISP.2015.7407956
Ruiqi Zhao, Yan Wang, and Aleix M. Martinez. 2018. A Simple, Fast and Highly-Accurate Algorithm to Recover 3D Shape from 2D Landmarks on a Single Image. IEEE Transactions on Pattern Analysis and Machine Intelligence 40, 12 (2018), 3059–3066. https://doi.org/10.1109/TPAMI.2017.2772922
Xiaoming Zhao and Shiqing Zhang. 2016. A Review on Facial Expression Recognition: Feature Extraction and Classification. IETE Technical Review 33, 5 (2016), 505–517. https://doi.org/10.1080/02564602.2015.1117403
Publicado
23/10/2023
Como Citar
PERSONA, Leandro; MELONI, Fernando; MACEDO, Alessandra Alaniz.
An accurate real-time method to detect the smile facial expression. In: BRAZILIAN SYMPOSIUM ON MULTIMEDIA AND THE WEB (WEBMEDIA), 29. , 2023, Ribeirão Preto/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
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p. 46–55.
